Methods, systems, and mediums for monitoring gas leakage safety based on internet of things

ABSTRACT

The present disclosure provides a method, system, and medium for monitoring smart gas leakage safety based on an Internet of Things system. The method is executed by a processor in a smart gas safety management platform of a system for monitoring smart gas leakage safety based on an Internet of Things system. The method includes: obtaining regional data and industrial gas data of a workshop to be monitored; determining a key sub-region based on the regional data and the industrial gas data; determining at least one recommended monitoring site based on environmental data of the key sub-region. The embodiment of the present disclosure reasonably and reliably determines the recommended monitoring site for different complex environments based on data obtained from an Internet of Things system, henceforth improving sensitivity and accuracy of monitoring of gas leakage safety.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.202310586674.3, filed on May 24, 2023, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of gas monitoring, and inparticular, to a method, system, and medium for monitoring smart gasleakage safety based on an Internet of Things system.

BACKGROUND

With the popularization of gas usage, gas has been widely integratedinto daily production and life. However, there are still certaintechnical difficulties in monitoring of gas leakage safety in regionssuch as industrial processing workshops and production plants. Accordingto existing standards and regulations, an installation location of amonitoring device should be determined according to a gas density andwind direction, and avoid installing the monitoring device in a placewhere the airflow is too large. However, the standards often only give areasonable range (e.g., a height range of the factory, a distance rangebetween the monitoring device and the ventilation port), and in theactual installation, it is often necessary to be guided by technicalpersonnel with rich installation experience which is time-consuming andlabor-intensive. And due to human factors and a complex and volatileenvironment, the installation parameters may have poor adaptability.

Therefore, it is hoped to provide a method, system, and medium formonitoring smart gas leakage safety based on an Internet of Thingssystem, which can reasonably and reliably determine recommendedmonitoring sites for different complex environments based on dataobtained from an Internet of Things system, and improve the accuracy andefficiency of monitoring of gas leakage safety.

SUMMARY

One or more embodiments of the present disclosure provide a method formonitoring smart gas leakage safety based on an Internet of Thingssystem, wherein the method is executed by a processor in a smart gassafety management platform of a system for managing smart gas leakagesafety based on an Internet of Things system, the method includes:obtaining regional data and industrial gas data of a workshop to bemonitored; determining a key sub-region based on the regional data andthe industrial gas data; determining at least one recommended monitoringsite based on environmental data of the key sub-region.

One or more embodiments of the present disclosure provide a system formonitoring smart gas leakage safety based on an Internet of Thingssystem, wherein the system includes a smart gas user platform, a smartgas service platform, a smart gas safety management platform, and asmart gas indoor device sensor network platform, a smart gas indoordevice object platform; the smart gas safety management platform isconfigured to obtain the regional data and the industrial gas data ofthe workshop to be monitored; determine the key sub-region based on theregional data and the industrial gas data; determine the at least onerecommended monitoring site based on the environmental data of the keysub-region.

One or more embodiments of the present disclosure provide acomputer-readable non-transitory storage medium storing computerinstructions, wherein a computer realizes the method for monitoringsmart gas leakage safety based on an Internet of Things system accordingto claim 1 when reading computer instructions stored in the medium.

In some embodiments of the present disclosure, based on data obtained byan Internet of Things system, the recommended monitoring sites arereasonably and reliably determined for different complex environments,which not only avoids resources waste caused by deploying a large numberof monitoring devices, but also ensures timely monitoring of gasleakage, effectively improving the efficiency and accuracy of monitoringof gas leakage safety, and ensuring the safety of gas usage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an exemplary system formonitoring smart gas leakage safety based on an Internet of Thingssystem according to some embodiments of the present disclosure;

FIG. 2 is a flowchart illustrating an exemplary process for monitoringsmart gas leakage based on an Internet of Things system according tosome embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary key sub-regionprediction model according to some embodiments of the presentdisclosure;

FIG. 4 is a schematic diagram illustrating an exemplary diffusion modelaccording to some embodiments of the present disclosure;

FIG. 5 is a schematic diagram illustrating an exemplary map ofrecommended monitoring sites and a risk assessment model according tosome embodiments of the present disclosure.

DETAILED DESCRIPTION

The drawings that need to be used in the description of the embodimentswill be briefly introduced below. The drawings do not represent allembodiments.

The words “a”, “an”, “one” and/or “the” are not intended to refer to thesingular and may include the plural unless the context clearly indicatesan exception. In general, the terms “comprise” and “include” imply theinclusion only of clearly identified steps and elements that do notconstitute an exclusive listing. A method or device may also includeother steps or elements.

It should be understood that in order to facilitate the description ofthe present disclosure, positional relationship indicated by the terms“center”, “upper surface”, “lower surface”, “upper”, “lower”, “top”,“bottom”, “inner”, “outer”, “axial”, “radial”, “peripheral”, “external”and so on is based on positional relationship shown in the attacheddrawings, rather than indicating that device, component, or unit musthave a specific positional relationship, which is not intended to limitthe scope of the present disclosure. However, the words may be replacedby other expressions if other words can achieve the same purpose.

FIG. 1 is a schematic diagram illustrating an exemplary system formonitoring smart gas leakage safety based on an Internet of Thingssystem according to some embodiments of the present disclosure. As shownin FIG. 1 , the system may include a smart gas user platform 110, asmart gas service platform 120, a smart gas safety management platform130, a smart gas indoor device sensor network platform 140, and a smartgas indoor device object platform 150 that are connected in sequence.

The smart gas user platform 110 may be a platform for interacting with auser. In some embodiments, the smart gas user platform 110 may beconfigured as a terminal device.

In some embodiments, the smart gas user platform 110 may include a gasuser sub-platform 111 and a supervision user sub-platform 112.

The gas user sub-platform 111 may be a platform that provides a gas userwith data related to gas usage and a solution to a gas problem. In someembodiments, the gas user sub-platform 111 may correspond to andinteract with a smart gas usage service sub-platform 121 to obtainservice for safe gas usage.

The supervision user sub-platform 112 may be a platform for asupervision user to supervise an operation of the entire system. In someembodiments, the supervision user sub-platform 112 may correspond to andinteract with a smart supervision service sub-platform 122 to obtainservice for safety supervision.

The smart gas service platform 120 may be a platform for communicatingthe user's need and control information. The smart gas service platform120 may obtain gas information from the smart gas management platform130 and send the gas information to the smart gas user platform 110.

In some embodiments, the smart gas service platform 120 may include thesmart gas usage service sub-platform 121 and the smart supervisionservice sub-platform 122.

The smart gas usage service sub-platform 121 may be a platform forproviding gas usage service for the gas user.

The smart supervision service sub-platform 122 may be a platform forsatisfying a supervision need of a supervision user.

The smart gas management platform 130 may be a platform that integratesand coordinates a connection and collaboration between the functionalplatforms, gathers all information of an Internet of Things system, andprovides functions of perception management and control management foran operating system of the Internet of Things system.

In some embodiments, the smart gas safety management platform 130 may beconfigured to obtain regional data and industrial gas data of a workshopto be monitored; determine a key sub-region based on the regional dataand the industrial gas data; determine at least one recommendedmonitoring site based on environmental data of the key sub-region.

More information about the regional data, the industrial gas data, thekey sub-region, the environmental data, and the recommended monitoringsite can be found in FIG. 2 , FIG. 3 , FIG. 4 , FIG. 5 , and relateddescriptions thereof.

In some embodiments, the smart gas management platform 130 may include asmart gas indoor safety management sub-platform 131 and a smart gas datacenter 132.

The smart gas indoor safety management sub-platform 131 may include anintrinsic safety monitoring management module 1311, an informationsafety monitoring management module 1312, a function monitoring safetymanagement module 1313, and an indoor safety inspection managementmodule 1314.

In some embodiments, the intrinsic safety monitoring management module1311 may include monitoring of mechanical leakage, electrical powerconsumption (e.g., intelligent control power consumption, communicationpower consumption), valve control, and other gas explosion safety.

In some embodiments, the information safety monitoring management module1312 may include monitoring of abnormal data, illegal deviceinformation, illegal access, or the like.

In some embodiments, the function monitoring management module 1313 mayinclude safety monitoring of functions such as a long-unused function,continuous flow timeout, flow overload, abnormally large flow,abnormally small flow, low air pressure, strong magnetic interference,low voltage, or the like.

In some embodiments, the indoor safety inspection management module 1314may include managing a safety inspection time warning, safety inspectionstatus, and safety problems of the gas user's indoor device.

The smart gas data center 132 may be configured to store and manage alloperation information of a system 100 for monitoring smart gas leakagesafety based on an Internet of Things system. In some embodiments, thesmart gas data center may be configured as a storage device for storingdata related to the monitoring of gas leakage safety or the like.

In some embodiments, the smart gas safety management platform 130 mayperform information interaction with the smart gas service platform 120and the smart gas indoor device sensor network platform 140 through thesmart gas data center 132, and the smart gas indoor safety managementsub-platform 131 obtains and feeds back safety management data of anindoor device from the smart gas data center 132, and the smart gas datacenter 132 summarizes and stores all operation data of the system.

In some embodiments, the smart gas indoor device sensor network platform140 may be a platform for managing perception and communication. In someembodiments, the smart gas indoor device sensing network platform 140may be configured as a communication network and a gateway.

In some embodiments, the smart gas indoor device sensing networkplatform 140 may include network management 141, protocol management142, instruction management 143, and data analysis 144.

The smart gas indoor device object platform 150 may be a platform forgenerating perception information and executing control information. Insome embodiments, the smart gas indoor device object platform 150 may beconfigured as various types of devices, including an indoor gas device(e.g., a gas meter, a monitoring device, a valve control device) or thelike.

In some embodiments, the smart gas indoor device object platform 150 mayinclude a fair metering device object sub-platform 151, a safetymonitoring device object sub-platform 152, and a safety valve controldevice object sub-platform 153, and the smart gas indoor device objectplatform 150 may obtain gas usage-related information through the objectsub-platforms.

In some embodiments of the present disclosure, the system 100 formanaging smart gas leakage safety based on an Internet of Things systemcan form a closed loop of information operation between the smart gasindoor device object platform 150 and the smart gas user platform 110,and coordinates and operates regularly under the smart gas safetymanagement platform 130, so as to realize the informatization andintellectualization of gas safety management.

FIG. 2 is a flowchart illustrating an exemplary process for monitoringsmart gas leakage based on an Internet of Things system according tosome embodiments of the present disclosure. As shown in FIG. 2 , aprocess 200 includes the following steps. In some embodiments, theprocess 200 may be executed by a processor in the smart gas safetymanagement platform 130.

step 210, obtaining regional data and industrial gas data of a workshopto be monitored.

The regional data refers to relevant information and data of theworkshop to be monitored. The regional data may include a structure,area, floor height, and enclosure of the workshop.

The industrial gas data refers to gas-related data of the workshop to bemonitored. The industrial gas data may include a distribution situationof gas pipelines and gas devices in the workshop, a density of the gaspipelines, a frequency and duration of usage of the gas devices, or thelike.

In some embodiments, a smart gas indoor device object platform 150 mayobtain the industrial gas data, and upload the industrial gas data to asmart gas data center 132 through a smart gas indoor device sensornetwork platform 140, and the processor may obtain the industrial gasdata from the smart gas data center 132. More information about thesmart gas indoor device object platform 150, the smart gas indoor devicesensor network platform 140, and the smart gas data center 132 can befound in FIG. 1 and related descriptions thereof.

step 220, determining a key sub-region based on the regional data andthe industrial gas data.

The key sub-region refers to a region where a monitoring device needs tobe installed or changed in the workshop to be monitored.

In some embodiments, the processor may determine the key sub-regionbased on a preset rule, the regional data, and the industrial gas data.For example, the processor may determine a region in the workshop wherethe region is smaller than a regional threshold with a relativelyairtight space, a dense distribution of gas pipelines, and frequentlylong-used gas devices as the key sub-region.

In some embodiments, the processor may determine the key sub-regionthrough the following steps S11 to S13.

step S11, determining a plurality of grid regions by performing gridprocessing on the workshop to be monitored based on the regional dataand the industrial gas data.

The grid processing refers to a process of dividing a three-dimensionalspatial area of the entire workshop into a limited number of sub-spaceregions with an equal volume.

In some embodiments, after the grid processing is performed on theworkshop to be monitored, grid regions that do not contain anygas-related facilities (e.g., a gas pipeline, a gas device) may beeliminated.

step S12, obtaining environmental data of at least one grid region amongthe plurality of grid regions.

The environmental data refers to relevant data of an environment in agrid region. The environmental data may include a wind directioncondition such as a wind direction change, air velocity, temperature,humidity, or the like.

step S13, determining the key sub-region based on the environmental dataof the at least one grid region.

In some embodiments, the processor may determine the key sub-region byquerying a table based on the environmental data of the at least onegrid region. For example, the processor may obtain environmental datacorresponding to historical key sub-regions and construct a table basedon the environmental data, and if environmental data of a grid region isthe same as the environmental data in the table, the grid region isdetermined as the key sub-region.

In some embodiments, the processor may determine a range of keysub-region through a key sub-region prediction model based on theenvironmental data of the at least one grid region. More informationabout the key sub-region prediction model can be found in FIG. 3 andrelated descriptions thereof.

The range of key sub-region refers to a coordinate range of keysub-regions in the workshop.

In some embodiments, the processor may determine the key sub-regionbased on the range of key sub-region. For example, the processor maydetermine a grid region that falls within the range of key sub-region asthe key sub-region.

In some embodiments of the present disclosure, performing the gridprocessing on the workshop to determine the key sub-region canpreliminarily screen out invalid grid regions, reduce a volume of datato be processed, and improve the work efficiency of the platform;moreover, determining the key sub-region based on the environmental datacan fully consider an impact of external environmental factors on amonitoring result of gas leakage safety, and improve the accuracy of adetermined key sub-region.

step 230: determining at least one recommended monitoring site based onenvironmental data of the key sub-region.

The recommended monitoring site refers to a recommended location or arange of locations for installing a gas monitoring device.

In some embodiments, the processor may determine the at least onerecommended monitoring site by comparing and analyzing currentenvironmental data of the key sub-region with historical data. Forexample, the processor may obtain historical environmental data andhistorical locations of monitoring sites of a same key sub-region, anddetermine a historical location of a monitoring site whose correspondinghistorical environmental data is as same as the current environmentaldata as a location of the recommended monitoring site.

In some embodiments, the processor may determine the at least onerecommended monitoring site through the following steps S21 and S22.

step S21, determining at least one set of diffusion trend data of thekey sub-region based on the environmental data of the key sub-region.

The diffusion trend data may include a diffusion speed of gas in variousdirections.

In some embodiments, after the processor performs the grid processing onthe workshop to be monitored, a coordinate system is formed based on theworkshop to be monitored, and the diffusion trend data may include thediffusion speed on three directions of x-axis, y-axis, and z-axis of thecoordinate system.

In some embodiments, gas path data of a plurality of consecutive gridregions may constitute diffusion trend data of gas. For example, pathdata of gas flowing from one grid region to a next grid region mayconstitute the diffusion trend data of gas.

In some embodiments, the processor may predict the at least one set ofdiffusion trend data of the key sub-region through a diffusion modelbased on the environmental data of the key sub-region.

More information about the diffusion model can be found in FIG. 4 andrelated descriptions thereof.

In some embodiments, the at least one set of diffusion trend data isalso related to an opening and closing situation of doors and windows inthe workshop to be monitored. For example, when the doors and windowsare open, due to an influence of airflow, the diffusion speed of gas ina certain direction is accelerated in the diffusion trend data.

In some embodiments, a relationship between the opening and closingsituation of doors and windows in the workshop to be monitored may berepresented by a Boolean value. If the doors and windows are open, theBoolean value is 1, and if the doors and windows are closed, the Booleanvalue is 0.

In some embodiments of the present disclosure, further considering theopening and closing situation of doors and windows when determining thediffusion trend data can avoid a misjudgment of gas leakage caused bychanges in the diffusion trend data for opening doors and windows.

step S22: determining the at least one recommended monitoring site basedon the at least one set of diffusion trend data of the key sub-region.

In some embodiments, if a gas leakage occurs at a certain location,leaked gas may gather at this location due to the lack of aircirculation. Therefore, the processor may determine a locationcorresponding to a set of diffusion trend data in which the diffusionspeed of gas in all directions is relatively small (e.g., smaller than aspeed threshold) as the recommended monitoring site based on at theleast one set of diffusion trend data of the key sub-region.

In some embodiments, the processor may determine an upstream location ofgas diffusion as the recommended monitoring site based on a diffusiondirection in the diffusion trend data. For example, in the diffusiontrend data, if the diffusion direction is from a location A to alocation B, then the location A is the upstream location, and theprocessor may determine the location A as the recommended monitoringsite.

In some embodiments, at least one monitoring device for gas leakage islocated on a combined slide, and the at least one monitoring devicemoves on the combined slide. In some embodiments, after the processorperforms the grid processing on the workshop to be monitored, thecoordinate system is formed based on the workshop to be monitored, andthe combined slide may include a plurality of sets of combined slides onthe directions of x-axis, y-axis, and z-axis, so that the monitoringdevice may slide in different dimensional directions.

In some embodiments, the processor may determine an adaptive location ofat least one monitoring device on the at least one recommendedmonitoring site based on the environmental data, the at least one set ofdiffusion trend data, and gas usage data of the key sub-regioncontinuously. For example, the processor may adjust a height of themonitoring device according to a wind force and wind direction indifferent seasons, a direction of airflow in the key sub-region, and adensity change of gas used in the workshop to be monitored.

The gas usage data refers to relevant information and data of gas usedby the workshop to be monitored. The gas usage data may include a type,density, flow rate, etc. of gas used.

In some embodiments, the processor may continuously process the gasusage data and the environmental data of the key sub-region through thediffusion model, predict the at least one set of diffusion trend data ofthe key sub-region, and determine at least one new recommendedmonitoring site based on the diffusion trend data, and adaptively adjustthe monitoring device to the new recommended monitoring site.

More information about the diffusion model can be found in FIG. 4 andrelated descriptions thereof. More details on determining therecommended monitoring site based on the diffusion trend data can befound in step S22.

In some embodiments of the present disclosure, according to real-timeenvironmental data and real-time diffusion trend data of the keysub-region, a location of the recommended monitoring site iscontinuously adjusted, and the monitoring device is automatically moved,so that the monitoring device can flexibly change the monitoringlocation according to an actual situation, henceforth improving autilization rate of resources and ensuring a timely monitoring of siteswhere a gas leakage may occur.

In some embodiments, the recommended monitoring site may include atleast one mandatory turn-on site and at least one optional turn-on site.

The mandatory turn-on site refers to a location where the monitoringdevice must be installed and turned on. The optional turn-on site refersto a location where the monitoring device is turned on demand accordingto an actual need.

In some embodiments, an actual requirement for determining whether amonitoring device at the optional turn-on site is turned on or not mayinclude an actual situation of production and gas usage of a workshop.

In some embodiments, a location of the optional turn-on site is relativeto a location of the mandatory turn-on site. For example, if there aremany optional turn-on sites in a certain region, one mandatory turn-onpoint may be appropriately set.

In some embodiments of the present disclosure, by setting the mandatoryturn-on site and the optional turn-on site, the monitoring device isturned on for a long time for a location with a higher demand whileturned on as needed according to an actual situation for a location witha lower demand. This can ensure that no locations where gas leakage mayoccur may be missed while reducing monitoring costs and a volume of dataprocessed by the platform; moreover, the optional turn-on site canassist the mandatory turn-on site to monitor whether the gas leakageoccurs, which further improves the accuracy of a monitoring result ofthe gas leakage safety and effectively guarantees the safety of gasusage.

In some embodiments, the processor may evaluate a risk value of a set ofturn-on sites.

The set of turn-on sites refers to a set formed by locations ofmonitoring devices that have been turned on at the recommendedmonitoring site.

The risk value refers to an index to judge whether the gas leakage canbe monitored timely and accurately. The smaller the risk value, thetimelier and more accurate the monitoring of the gas leakage.

In some embodiments, different sets of turn-on sites correspond todifferent risk values. For example, there are three optional turn-onsites A, B, and C. When only turning on a monitoring device at A, acorresponding risk value may be 90%, when turning on monitoring devicesat A and B, a corresponding risk value may be 50%, when turning onmonitoring devices at B and C, a corresponding risk value may be 40%.

In some embodiments, the processor may determine turn-on sites includedin a set of turn-on sites whose risk value satisfies a risk condition asthe mandatory turn-on sites, and determine monitoring sites other thanthe mandatory turn-on sites among the recommended monitoring site as theoptional turn-on sites.

In some embodiments, the risk condition may be determined based on apriori experience. For example, the risk condition may be that the riskvalue is less than a risk threshold (e.g., 50%).

In some embodiments, on the premise that the risk value satisfies therisk condition, the optional turn-on site is also related to otherfactors. For example, the optional turn-on site is also related toeconomic benefits. Under the premise that the risk value satisfies therisk condition, the fewer the count of optional turn-on sites, the lowerthe production cost.

In some embodiments, the processor may construct a map of recommendedmonitoring site based on the at least one recommended monitoring site;determine the risk value of the set of turn-on sites through a riskassessment model based on the map of recommended monitoring site.

More information on the map of recommended monitoring site and the riskassessment model can be found in FIG. 5 and related descriptionsthereof.

In some embodiments, the processor may determine a monitoring devicethat needs to be turned on at the at least one optional turn-on sitebased on the risk value. For example, when the risk value falls tosatisfy the risk condition, the processor may determine the monitoringdevice that needs to be turned on at the at least one optional turn-onsite based on a turn-on situation of monitoring devices at the optionalturn-on site corresponding to the risk value.

In some embodiments of the present disclosure, determining themonitoring device that needs to be turned on at the optional turn-onsite based on the risk value can accurately determine a monitoringdevice that needs to be opened, which can not only ensure timely andcomprehensive monitoring of the gas leakage but also avoid resourceswaste.

It should be noted that the above description about the process 200 isonly for illustration and description, and does not limit the scope ofapplication of the present disclosure herein. For those skilled in theart, various modifications and changes may be made to the process 200under the guidance of the present disclosure. However, suchmodifications and changes are still within the scope of the presentdisclosure.

FIG. 3 is a schematic diagram illustrating an exemplary key sub-regionprediction model according to some embodiments of the presentdisclosure.

In some embodiments, as shown in FIG. 5 , a processor may determine atleast one set of diffusion trend data 320 of a grid region based onenvironmental data 310 of the grid region.

A manner of determining the at least one set of diffusion trend data ofthe at least one grid region based on the environmental data of the atleast one grid region is the same as a manner of determining at leastone set of diffusion trend data of a key sub-region based onenvironmental data of the key sub-region. More information about theenvironmental data, the diffusion trend data, and determining thediffusion trend data based on the environmental data can be found inFIG. 2 , FIG. 4 , and related descriptions thereof.

In some embodiments, the processor may determine a range of keysub-region based on the at least one set of diffusion trend data,regional data, and industrial gas data of the at least one grid regionthrough a key sub-region prediction model. More information about theregional data and the industrial gas data can be found in FIG. 2 andrelated descriptions thereof.

The key sub-region prediction model refers to a model configured todetermine the key sub-region. In some embodiments, the key sub-regionprediction model may be a machine learning model, such as a neuralnetwork (NN) model or the like. In some embodiments, the key sub-regionprediction model may be other machine learning models or a combinationthereof.

In some embodiments, an input of a key sub-region prediction model 350may include the at least one set of diffusion trend data 320, regionaldata 330, and industrial gas data 340 of the grid region, and an outputmay include a range of key sub-region 360.

In some embodiments, the processor may determine a key sub-region 370based on the range of key sub-region 360. More information about therange of key sub-region and the key sub-region can be found in FIG. 2and related descriptions thereof.

In some embodiments, the key sub-region prediction model may be obtainedby training historical data separately. In some embodiments, a firsttraining sample for training the key sub-region prediction model mayinclude at least one set of diffusion trend data, regional data, andindustrial gas data of a plurality of sample grid regions; a first labelcorresponding to the first training sample is whether a sample gridregion is the key sub-region. If the sample grid region is the keysub-region (i.e., it is necessary to install or change a monitoringdevice), the first label is 1, otherwise, it is 0.

In some embodiments of the present disclosure, after grid processing isperformed on a workshop, for each grid, determining the diffusion trenddata based on the environmental data can fully take into account aninfluence of different environmental factors on gas diffusion; moreover,by processing the diffusion trend data, the regional data, and theindustrial gas data through the key sub-region prediction model todetermine the key sub-region, efficiency and accuracy of predicting thekey sub-region can be improved.

FIG. 4 is a schematic diagram illustrating an exemplary diffusion modelaccording to some embodiments of the present disclosure;

In some embodiments, the processor may process environmental data of akey sub-region through a diffusion model to predict at least one set ofdiffusion trend data of the key sub-region.

More information about the key sub-region, the environmental data, andthe diffusion trend data can be found in FIG. 2 and related descriptionsthereof.

The diffusion model refers to a model configured to predict the at leastone set of diffusion trend data of the key sub-region. In someembodiments, the diffusion model may be a machine learning model, suchas a neural network (NN) model or the like. In some embodiments, thediffusion model may be other machine learning models or a combinationthereof.

In some embodiments, an input of a diffusion model 430 may at leastinclude gas usage data 410 and environmental data 420 of the keysub-region, and an output may include at least one set of diffusiontrend data 440 of the key sub-region. In some embodiments, the at leastone set of diffusion trend data 440 output of the key sub-regionincludes a gas diffusion speed in directions of x-axis, y-axis, andz-axis.

In some embodiments, the input of the diffusion model 430 may alsoinclude an opening and closing situation of doors and windows in aworkshop to be monitored.

More information about the gas usage data and the opening and closingsituation of doors and windows in the workshop to be monitored can befound in FIG. 2 and related descriptions thereof.

In some embodiments, the diffusion model may be obtained by traininghistorical data separately. In some embodiments, a second trainingsample for training the diffusion model may include environmental dataand sample gas usage data of a plurality of sample key sub-regions; asecond label corresponding to the second training sample is actualdiffusion trend data of the sample key sub-region.

In some embodiments of the present disclosure, predicting the at leastone set of diffusion trend data of the key sub-region by the diffusionmodel based on the gas usage data and the environmental data of the keysub-region, and further using the opening and closing situation of doorsand windows in the workshop to be monitored as an input of the diffusionmodel can improve the accuracy of determining the diffusion trend data.

FIG. 5 is a schematic diagram illustrating an exemplary map ofrecommended monitoring sites and a risk assessment model according tosome embodiments of the present disclosure.

In some embodiments, as shown in FIG. 5 , a processor may construct amap of recommended monitoring site 520 based on at least one recommendedmonitoring site 510.

The map of recommended monitoring site refers to a map that reflects arelationship between various factors in symbolic form based on therecommended monitoring site. The map of recommended monitoring siteincludes nodes and edges.

In some embodiments, the processor may randomly select a limited numberof recommended monitoring sites from the recommended monitoring sites toform the map of recommended monitoring site.

A node refers to a recommended monitoring site, and attributes of thenode include a height of a recommended monitoring site corresponding tothe node and industrial gas-related data. The height refers to a risingdistance of the recommended monitoring site in a vertical directionbased on the ground of a workshop to be monitored. The industrialgas-related data refers to a situation of distribution and usage of gaspipelines and gas devices near the recommended monitoring site (e.g., adistance from the recommended monitoring site is less than a distancethreshold).

An edge is configured to connect the nodes. In some embodiments, if adistance between recommended monitoring sites corresponding to two nodesis less than the distance threshold, the two nodes are connected by anedge. In some embodiments, an edge between connecting nodes is adirected edge, and a direction of the edge is a diffusion direction ofgas.

Attributes of the edge include a distance between recommended monitoringsites corresponding to nodes and the diffusion direction of gas.

In some embodiments, the processor may process the map of recommendedmonitoring site through a risk assessment model to determine a riskvalue.

The risk assessment model refers to a model configured to determine arisk value of a set of turn-on sites. In some embodiments, the riskassessment model may be a Graph Neural Networks (GNN) model. In someembodiments, the risk assessment model may be other machine learningmodels or a combination thereof.

In some embodiments, an input of a risk assessment model 530 may includea map of recommended monitoring site 520. In some embodiments, when themap of recommended monitoring site 520 is input into the risk assessmentmodel 530, recommended monitoring sites contained in the map ofrecommended monitoring site 520 are all regarded as turn-on sites. Insome embodiments, an output of the risk assessment model 530 may includea risk value 540 of a set of turn-on sites included in the map ofrecommended monitoring site 520. More information about the risk valuecan be found in FIG. 2 and related descriptions thereof.

In some embodiments, the risk assessment model may be obtained bytraining historical data separately. In some embodiments, a thirdtraining sample for training the risk assessment model may include atleast one map of sample recommended monitoring site constructed based ondifferent sample recommended monitoring sites; a third labelcorresponding to the third training sample is whether gas leakage can bedetected timely and accurately when monitoring devices are turned onaccording to recommended monitoring sites contained in the map of samplerecommended monitoring site. If the gas leakage can be detected, thelabel is 1, otherwise, it is 0.

In some embodiments of the present disclosure, constructing a map ofrecommended monitoring site based on the recommended monitoring sitescan fully take into account a relationship between different recommendedmonitoring sites; Moreover, processing the map of recommended monitoringsite based on the risk assessment model can predict the risk value moreaccurately and rationally.

One or more embodiments of the present disclosure provide acomputer-readable non-transitory storage medium storing computerinstructions, and when a computer reads the computer instructions in thestorage medium, the computer executes the method for monitoring smartgas leakage safety based on an Internet of Things system described inany one of the above-mentioned embodiments.

When the operations performed are described step by step in theembodiments of the present disclosure, unless otherwise specified, theorder of the steps can be changed, the steps can be omitted, and othersteps can also be included in the operation process.

The embodiments in the present disclosure are only for illustration anddescription, and do not limit the scope of application of the presentdisclosure. For those skilled in the art, various modifications andchanges can be made under the guidance of the present disclosure and thevarious modifications and changes are still within the scope of thepresent disclosure.

Certain features, structures, or characteristics in one or moreembodiments of the present disclosure may be properly combined.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about”, “approximate”, or “substantially”. Unless otherwisestated, the “about”, “approximate”, or “substantially” indicates thatthe stated figure allows for a variation of ±20%. Accordingly, in someembodiments, the numerical parameters and claims used in the presentdisclosure are approximations that can vary depending on the desiredcharacteristics of individual embodiments. Although the numerical rangesand parameters used in some embodiments of the present disclosure toconfirm the breadth of the range are approximations, in specificembodiments, such numerical values may be set as precisely aspracticable.

If there is any inconsistency or conflict between the descriptions,definitions, and/or use of terms in the cited materials in the presentdisclosure and the contents of the present disclosure, the descriptions,definitions, and/or use of terms in the present disclosure shallprevail.

What is claimed is:
 1. A method for monitoring smart gas leakage safetybased on an Internet of Things system, wherein the method is executed bya processor in a smart gas safety management platform of a system formonitoring smart gas leakage safety based on an Internet of Thingssystem, comprising: obtaining regional data and industrial gas data of aworkshop to be monitored; determining a key sub-region based on theregional data and the industrial gas data; and determining at least onerecommended monitoring site based on environmental data of the keysub-region.
 2. The method according to claim 1, wherein the determininga key sub-region based on the regional data and the industrial gas dataincludes: determining a plurality of grid regions by performing gridprocessing on the workshop to be monitored based on the regional dataand the industrial gas data; obtaining environmental data of at leastone grid region among the plurality of grid regions; and determining thekey sub-region based on the environmental data of the at least one gridregion.
 3. The method according to claim 2, wherein the determining thekey sub-region based on the environmental data of the at least one gridregion includes: determining a range of key sub-region through a keysub-region prediction model based on the environmental data of the atleast one grid region, and the key sub-region prediction model is amachine learning model; and determining the key sub-region based on therange of key sub-region.
 4. The method according to claim 3, wherein thedetermining a range of key sub-region through a key sub-regionprediction model based on the environmental data of the at least onegrid region includes: determining at least one set of diffusion trenddata of the at least one grid region based on the environmental data ofthe at least one grid region; and determining the range of keysub-region through the key sub-region prediction model based on the atleast one set of diffusion trend data, the regional data, and theindustrial gas data of the at least one grid region.
 5. The methodaccording to claim 1, wherein the determining at least one recommendedmonitoring site based on environmental data of the key sub-regionincludes: determining at least one set of diffusion trend data of thekey sub-region based on the environmental data of the key sub-region;and determining the at least one recommended monitoring site based onthe at least one set of diffusion trend data of the key sub-region. 6.The method according to claim 5, wherein at least one monitoring devicefor monitoring gas leakage is located on a combined slide, and the atleast one monitoring device moves on the combined slide; the methodfurther includes: determining an adaptive location of the at least onemonitoring device at the at least one recommended monitoring site basedon the environmental data, the at least one set of diffusion trend data,and gas usage data of the key sub-region.
 7. The method according toclaim 5, wherein the determining at least one set of diffusion trenddata of the key sub-region based on the environmental data of the keysub-region includes: predicting at least one set of diffusion trend dataof the key sub-region through a diffusion model based on theenvironmental data of the key sub-region, and the diffusion model is amachine learning model.
 8. The method according to claim 5, wherein theat least one recommended monitoring site includes at least one mandatoryturn-on site and at least one optional turn-on site.
 9. The methodaccording to claim 8, further comprising: evaluating a risk value of aset of turn-on sites; and determining the at least one mandatory turn-onsite and the at least one optional turn-on site based on the risk value.10. The method according to claim 9, wherein the evaluating a risk valueof a set of turn-on sites includes: constructing a map of recommendedmonitoring site based on the at least one recommended monitoring site;and determining the risk value through a risk assessment model based onthe map of recommended monitoring site, and the risk assessment model isa graph neural network model.
 11. A system for monitoring smart gasleakage safety based on an Internet of Things system, wherein the systemincludes a smart gas user platform, a smart gas service platform, asmart gas safety management platform, a smart gas indoor device sensornetwork platform, a smart gas indoor device object platform; the smartgas safety management platform is configured to: obtain regional dataand industrial gas data of a workshop to be monitored; determine a keysub-region based on the regional data and the industrial gas data; anddetermine at least one recommended monitoring site based onenvironmental data of the key sub-region.
 12. The system according toclaim 11, wherein the smart gas safety management platform is furtherconfigured to: determine a plurality of grid regions grid by performinggrid processing on the workshop to be monitored based on the regionaldata and the industrial gas data; obtain environmental data of at leastone grid region among the plurality of grid regions; and determine thekey sub-region based on the environmental data of the at least one gridregion.
 13. The system according to claim 12, wherein the smart gassafety management platform is further configured to: determine a rangeof key sub-region range through a key sub-region prediction model basedon the environmental data of the at least one grid region, and the keysub-region prediction model is a machine learning model; and determinethe key sub-region based on the range of key sub-region.
 14. The systemaccording to claim 13, wherein the smart gas safety management platformis further configured to: determine at least one set of diffusion trenddata of the at least one grid region based on the environmental data ofthe at least one grid region; and determine the range of key sub-regionthrough the key sub-region prediction model based on the at least oneset of diffusion trend data, the regional data, and industrial gas dataof the at least one grid region.
 15. The system according to claim 11,wherein the smart gas safety management platform is further configuredto: determine the at least one set of diffusion trend data of the keysub-region based on the environmental data of the key sub-region; anddetermine at least one recommended monitoring site based on the at leastone set of diffusion trend data of the key sub-region.
 16. The systemaccording to claim 15, wherein at least one monitoring device formonitoring gas leakage is located on a combined slide, and the at leastone monitoring device moves on the combined slide; and the smart gassafety management platform is further configured to: determine anadaptive location of the at least one monitoring device at the at leastone recommended monitoring site based on the environmental data, the atleast one set of diffusion trend data, and gas usage data of the keysub-region.
 17. The system according to claim 15, wherein the smart gassafety management platform is further configured to: predict the atleast one set of diffusion direction data of the key sub-region througha diffusion model based on the environmental data of the key sub-region,and the diffusion model is a machine learning model.
 18. The systemaccording to claim 15, wherein the at least one recommended monitoringsite includes at least one mandatory turn-on site and at least oneoptional turn-on site, and the smart gas safety management platform isalso configured to: evaluate a risk value of a set of turn-on sites;determine the at least one mandatory turn-on site and the at least oneoptional turn-on site based on the risk value.
 19. The system accordingto claim 18, wherein the smart gas safety management platform is furtherconfigured to: construct a map of recommended monitoring site based onthe at least one recommended monitoring site; determine the risk valuethrough a risk assessment model based on the map of recommendedmonitoring site, and the risk assessment model is a graph neural networkmodel.
 20. A computer-readable non-transitory storage medium storingcomputer instructions, wherein a computer realizes a method formonitoring smart gas leakage safety based on an Internet of Thingssystem according to claim 1 when the computer instructions are executedby a processor.